We also present the quantitative results in Fig. REF . The angle error is computed by error = ground truth - estimated angle. We then add or subtract \(360^{\circ }\)  to the error to fit it in the range of \([-180,180]^{\circ }\)  if the absolute value of the error is greater than \(180^{\circ }\) . For example, when the error is \(359^{\circ }\) , we subtract it by \(360^{\circ }\)  and get \(-1^{\circ }\)  as the error. This is a reasonable setting adopted from the previous work [1]}. As evident from Fig. REF , the point-specific activation map significantly outperforms the overall map for cross-view orientation estimation. Point-specific activation based orientation estimation has over \(16\%\)  samples (a total of 8,884 test sample pairs in the CVUSA dataset) with angle error less than \(\pm 3.5^{\circ }\)  (the red bar at \(0^{\circ }\)  in Fig. REF ), while overall activation map based method has less than \(12\%\) . The result further validates the superiority of the point-specific map compared with the overall map.
Moreover, we also provide a demoPlease check the demo (the “Geo-localization" section) at https://github.com/Jeff-Zilence/Explain_Metric_Learning to show how the point-specific map changes according to the query pixel.
